Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/prime-radiant-inc/iterative-development/auditing-progressnpx skills add prime-radiant-inc/iterative-development --skill auditing-progressgit clone --depth 1 https://github.com/prime-radiant-inc/iterative-developmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prime-radiant-inc/iterative-development/auditing-progress)<a href="https://agentmods.dev/skills/prime-radiant-inc/iterative-development/auditing-progress"><img src="https://agentmods.dev/badge/skills/prime-radiant-inc/iterative-development/auditing-progress.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00044 | $0.00923 |
| Opus 5 | $0.00022 | $0.00462 |
| Sonnet 5 | $0.00009 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
auditing-progress scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditing Progress
Overview
Runs after every iteration as part of the planning cycle. Verifies behavior evidence quality in three tiers using parallel adversarial review (PAR) — two paired auditor subagents evaluate the same work in parallel with competitive framing.
The audit answers: "Does durable, reusable evidence exist at the correct seam for every externally observable behavior this iteration touched?"
When to Use
Invoked by iterative-development after every running-an-iteration call, before picking the next iteration.
Audit Process
1. Partition the audit into three tiers
Read the per-epic requirement files in docs/superpowers/iterations/requirements/, docs/superpowers/iterations/behavior-scenarios.md, and docs/superpowers/iterations/behavior-corpus.md:
- Tier 1 — Deep evidence: stories marked
done:ITER-<current>and scenarios added or updated in this iteration. Audit every AC and its proof obligation thoroughly. - Tier 2 — Impacted behavior: all existing scenarios whose owning stories had code changes in this iteration (even if those stories were completed in earlier iterations). Verify the scenarios still pass.
- Tier 3 — Sentinel corpus: all scenarios with run cadence
sentinelin the behavior corpus. Compare against the pre-iteration baseline fromrunning-an-iterationstep 3.
2. Dispatch paired auditor subagents (PAR)
Following the PAR methodology in skills/shared/parallel-adversarial-review.md:
- Build the auditor prompt using
auditor-subagent-prompt.md. Include ALL THREE tiers:- Tier 1: full story cards with proof obligations + new/changed scenario cards
- Tier 2: impacted scenario cards + their current test results
- Tier 3: sentinel scenario IDs + baseline results + current results
- Wrap in competitive framing from
skills/shared/par-reviewer-wrapper.md - Dispatch TWO auditor subagents in parallel
- Wait for both to return
3. Aggregate findings
Following PAR aggregation rules:
- Same finding from both auditors → one finding, high confidence
- Finding from only one auditor → separate finding, still actionable
- Severity disagreement → take the more severe assessment, always fix it
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 80 lines · 44 tokens per session scan A b8a1897eb817
auditing-progress is a skill published in the GitHub repository prime-radiant-inc/iterative-development (177 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 923 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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